Genome-Wide Identification of the ABC Gene Family and Its Expression in Response to the Wood Degradation of Poplar in Trametes gibbosa

Wood-rotting fungi’s degradation of wood not only facilitates the eco-friendly treatment of organic materials, decreasing environmental pollution, but also supplies crucial components for producing biomass energy, thereby reducing dependence on fossil fuels. The ABC gene family, widely distributed in wood-rotting fungi, plays a crucial role in the metabolism of lignin, cellulose, and hemicellulose. Trametes gibbosa, as a representative species of wood-rotting fungi, exhibits robust capabilities in wood degradation. To investigate the function of the ABC gene family in wood degradation by T. gibbosa, we conducted a genome-wide analysis of T. gibbosa’s ABC gene family. We identified a total of 12 Tg-ABCs classified into four subfamilies (ABCA, ABCB, ABCC, and ABCG). These subfamilies likely play significant roles in wood degradation. Scaffold localization and collinearity analysis results show that Tg-ABCs are dispersed on scaffolds and there is no duplication of gene sequences in the Tg-ABCs in the genome sequence of T. gibbosa. Phylogenetic and collinearity analyses of T. gibbosa along with four other wood-rotting fungi show that T. gibbosa shares a closer phylogenetic relationship with its same-genus fungus (Trametes versicolor), followed by Ganoderma leucocontextum, Laetiporus sulphureus, and Phlebia centrifuga in descending order of phylogenetic proximity. In addition, we conducted quantitative analyses of Tg-ABCs from T. gibbosa cultivated in both woody and non-woody environments for 10, 15, 20, 25, 30, and 35 days using an RT-qPCR analysis. The results reveal a significant difference in the expression levels of Tg-ABCs between woody and non-woody environments, suggesting an active involvement of the ABC gene family in wood degradation. During the wood degradation period of T. gibbosa, spanning from 10 to 35 days, the relative expression levels of most Tg-ABCs exhibited a trend of increasing, decreasing, and then increasing again. Additionally, at 20 and 35 days of wood degradation by T. gibbosa, the relative expression levels of Tg-ABCs peak, suggesting that at these time points, Tg-ABCs exert the most significant impact on the degradation of poplar wood by T. gibbosa. This study systematically reveals the biological characteristics of the ABC gene family in T. gibbosa and their response to woody environments. It establishes the foundation for a more profound comprehension of the wood-degradation mechanism of the ABC gene family and provides strong support for the development of more efficient wood-degradation strategies

The Tracking and Frequency Measurement of the Sway of Leafless Deciduous Trees by Adaptive Tracking Window Based on MOSSE

The tree sway frequency is an important part of the dynamic properties of trees. In order to obtain trees sway frequency in wind, a method of tracking and measuring the sway frequency of leafless deciduous trees by adaptive tracking window based on MOSSE was proposed. Firstly, an adaptive tracking window is constructed for the observed target. Secondly, the tracking method based on Minimum Output Sum Of Squared Error Filter (MOSSE) is used to track tree sway. Thirdly, Fast Fourier transform was used to analyze the horizontal sway velocity of the target area on the trees, and the sway frequency was determined. Finally, comparing the power spectral densities (PSDs) of the x axis acceleration measured by the accelerometer and PSDs of the x axis velocity measured by the video, the fundamental sway frequency measured by the accelerometer is equal to the fundamental sway frequency measured by video. The results show that the video-based method can be used successfully for measuring the sway frequency of leafless deciduous trees

Research on Hyperspectral Image Reconstruction Based on GISMT Compressed Sensing and Interspectral Prediction

Hyperspectral remote-sensing images have the characteristics of large transmission data and high propagation requirements, so they are faced with transmission and preservation problems in the process of transmission. In view of this situation, this paper proposes a spectral image reconstruction algorithm based on GISMT compressed sensing and interspectral prediction. Firstly, according to the high spectral correlation of hyperspectral remote-sensing images, the hyperspectral images are grouped according to the band, and a standard band is determined in each group. The standard band in each group is weighted by the GISMT compressed sensing method. Then, a prediction model of the general band in each group is established to realize the remote-sensing image reconstruction in the general band. Finally, the difference between the actual measured value and the predicted value is calculated. According to the prediction algorithm, the corresponding difference vector is obtained and the predicted measured value is iteratively updated by the difference vector until the hyperspectral reconstructed image of the relevant general band is finally reconstructed. It is shown by experiments that this method can effectively improve the reconstruction effect of hyperspectral images

Smish: A Novel Activation Function for Deep Learning Methods

Activation functions are crucial in deep learning networks, given that the nonlinear ability of activation functions endows deep neural networks with real artificial intelligence. Nonlinear nonmonotonic activation functions, such as rectified linear units, Tan hyperbolic (tanh), Sigmoid, Swish, Mish, and Logish, perform well in deep learning models; however, only a few of them are widely used in mostly all applications due to their existing inconsistencies. Inspired by the MB-C-BSIF method, this study proposes Smish, a novel nonlinear activation function, expressed as f(x)=x·tanh[ln(1+sigmoid(x))], which could overcome other activation functions with good properties. Logarithmic operations are first used to reduce the range of sigmoid(x). The value is then calculated using the tanh operator. Inputs are ultimately used to multiply the previous value, thus exhibiting negative output regularization. Experiments show that Smish tends to operate more efficiently than Logish, Mish, and other activation functions on EfficientNet models with open datasets. Moreover, we evaluated the performance of Smish in various deep learning models and the parameters of its function f(x)=αx·tanh[ln(1+sigmoid(βx))], and where α = 1 and β = 1, Smish was found to exhibit the highest accuracy. The experimental results show that with Smish, the EfficientNetB3 network exhibits a Top-1 accuracy of 84.1% on the CIFAR-10 dataset; the EfficientNetB5 network has a Top-1 accuracy of 99.89% on the MNIST dataset; and the EfficientnetB7 network has a Top-1 accuracy of 91.14% on the SVHN dataset. These values are superior to those obtained using other state-of-the-art activation functions, which shows that Smish is more suitable for complex deep learning models

Intelligent Measurement of Frontal Area of Leaves in Wind Tunnel Based on Improved U-Net

Research on the aerodynamic characteristics of leaves is part of the study of wind-induced tree disasters and has relevance to plant biological processes. The frontal area, which varies with the structure of leaves, is an important physical parameter in studying the aerodynamic characteristics of leaves. In order to measure the frontal area of a leaf in a wind tunnel, a method based on improved U-Net is proposed. First, a high-speed camera was used to collect leaf images in a wind tunnel; secondly, the collected images were corrected, cut and labeled, and then the dataset was expanded by scaling transformation; thirdly, by reducing the depth of each layer of the encoder and decoder of U-Net and adding a batch normalization (BN) layer and dropout layer, the model parameters were reduced and the convergence speed was accelerated; finally, the images were segmented based on the improved U-Net to measure the frontal area of the leaf. The training set was divided into three groups in the experiment. The experimental results show that the MIoUs were 97.67%, 97.78% and 97.88% based on the improved U-Net training on the three datasets, respectively. The improved U-Net model improved the measurement accuracy significantly when the dataset was small. Compared with the manually labeled image data, the RMSEs of the frontal areas measured by the models based on the improved U-Net were 1.56%, 1.63% and 1.60%, respectively. The R2 values of the three measurements were 0.9993. The frontal area of a leaf can be accurately measured based on the proposed method

Intelligent Measurement of Frontal Area of Leaves in Wind Tunnel Based on Improved U-Net

Research on the aerodynamic characteristics of leaves is part of the study of wind-induced tree disasters and has relevance to plant biological processes. The frontal area, which varies with the structure of leaves, is an important physical parameter in studying the aerodynamic characteristics of leaves. In order to measure the frontal area of a leaf in a wind tunnel, a method based on improved U-Net is proposed. First, a high-speed camera was used to collect leaf images in a wind tunnel; secondly, the collected images were corrected, cut and labeled, and then the dataset was expanded by scaling transformation; thirdly, by reducing the depth of each layer of the encoder and decoder of U-Net and adding a batch normalization (BN) layer and dropout layer, the model parameters were reduced and the convergence speed was accelerated; finally, the images were segmented based on the improved U-Net to measure the frontal area of the leaf. The training set was divided into three groups in the experiment. The experimental results show that the MIoUs were 97.67%, 97.78% and 97.88% based on the improved U-Net training on the three datasets, respectively. The improved U-Net model improved the measurement accuracy significantly when the dataset was small. Compared with the manually labeled image data, the RMSEs of the frontal areas measured by the models based on the improved U-Net were 1.56%, 1.63% and 1.60%, respectively. The R2 values of the three measurements were 0.9993. The frontal area of a leaf can be accurately measured based on the proposed method

The Tracking and Frequency Measurement of the Sway of Leafless Deciduous Trees by Adaptive Tracking Window Based on MOSSE

The tree sway frequency is an important part of the dynamic properties of trees. In order to obtain trees sway frequency in wind, a method of tracking and measuring the sway frequency of leafless deciduous trees by adaptive tracking window based on MOSSE was proposed. Firstly, an adaptive tracking window is constructed for the observed target. Secondly, the tracking method based on Minimum Output Sum Of Squared Error Filter (MOSSE) is used to track tree sway. Thirdly, Fast Fourier transform was used to analyze the horizontal sway velocity of the target area on the trees, and the sway frequency was determined. Finally, comparing the power spectral densities (PSDs) of the x axis acceleration measured by the accelerometer and PSDs of the x axis velocity measured by the video, the fundamental sway frequency measured by the accelerometer is equal to the fundamental sway frequency measured by video. The results show that the video-based method can be used successfully for measuring the sway frequency of leafless deciduous trees

The Performance of Discriminative Tracking Algorithms for the Sway Frequency Measurement of <i>Betula platyphylla</i> Sukaczev (Individual Branch and Tree) under Artificial and Natural Excitation

The sway frequency is an important component of the dynamic characteristics of trees. Video-based methods can be used to measure the sway frequencies of trees. The key to successfully measuring tree sway frequency using video methods lies in whether the tracking method employed is appropriate. Based on six algorithms, i.e., Boosting, TLD, MIL, KCF, MOSSE and CSR-DCF, the tracking performance and accuracy of tree sway frequency measurements were investigated under two conditions: artificial excitation and environmental excitation. The results show that the following: (1) In terms of the tracking speed of tree sway, MOSSE > KCF > CSR-DCF > Boosting > MIL > TLD. (2) The TLD algorithm is not suitable for tree sway tracking. Boosting, MIL, MOSSE, KCF and CSR-DCF can be used for tree sway tracking. (3) Boosting, MIL and MOSSE are suitable for measuring the sway frequency of artificially excited branches and environmentally excited trees. (4) KCF and CSR-DCF algorithms are not suitable for the measurement of branch sway frequency under artificial excitation conditions but can be used for the measurement of tree sway frequency under environmental excitation conditions. However, it should be noted that this experiment only takes a Betula platyphylla Sukaczev tree and a Betula platyphylla Sukaczev branch as the research object to verify the effectiveness and feasibility of each tracking method, and does not verify the generalization ability of the above methods (on multiple tree species and multiple trees)